Webinar Reflection: Stats for Data Visualization Part 1

Thank you to Makeover Monday‘s Eva Murray and Andy Kriebel for allowing me to grace their BrightTALK air waves with my love language of statistics yesterday! If you missed it, check out the recording.

webinar 1

With 180 school days and 5 classes (plus seminar once/week), you can imagine a typical U.S. high school math teacher has the opportunity to instruct/lead between 780 and 930 lectures each year. After 14 years teaching students in Atlanta-area schools (plus those student-teaching hours, and my time as a TA at LSU), I’ve instructed somewhere in the ballpark of 12,000 to 13,500 lessons in my lifetime.

So let’s be honest. Yesterday I was nervous to lead my very first webinar. After all, I depend on my gift of crowd-reading to determine the pace (and the tone) of a presentation. Luckily, I’m an expert at laughing at my own jokes so after the first few slides (and figuring out the delay), I felt comfortable. So Andy and Eva, I am ready for the next webinar on December 20th — Audience, y’all can sign up here.

Fun Fact: In 6th grade I was in the same math class as Andy Kriebel’s sister-in-law. It was also the only year I ever served time in in-school suspension (but remember, correlation doesn’t imply causation).

Webinar Questions and Answers

I was unable to get to all the questions asked on the webinar but rest assured I will do my best to field those here.

  1. Q: Can you provide the dataset? A: Here’s a link to the 4Runner data I used for most of the webinar. Let me know if you’d like any others.
  2. Q: Do you have the data that produced the cartoon in the beginning slide? A: A surprising number of people reproduced the data and the curves from the cartoon within hours of its release. Here is one person’s reproduction in R from this blog post 
  3. Q: Do you have any videos on the basics of statistics? A: YES! My new favorite is Mr. Nystrom on YouTube, we use similar examples and he looks like he loves his job. For others, Google the specific topic along with the words “AP Statistics” for some of the best tutorials out there.
  4. Q: Could you explain about example A with r value -0.17, it seems as 0. A: The picture when r = -.17 is slightly negative — only slightly. This one is very tricky because we tend to think r = 0 if it’s not linear. But remember correlation is on a continuous scale of weak to positive – which means r = -.17 is still really, realAly weak. r = 0 is probably not very likely to be observed in real data unless the data creates a perfect square or circle, for example.
  5. Q: Question for Anna, does she also use Python, R, other stats tools? A: I am learning R! R-studio makes it easier. When I coach doctoral candidates on dissertation defense I use SPSS and Excel; one day I will learn Python. Of course, I am an expert on the TI-84. Stop laughing.TI84

6. Q: So with nonlinear regression [is it] better to put the prediction on the y-axis? A: With linear and nonlinear regression, the variable you want to predict will always be your y-axis (dependent) variable. That variable is always depicted with a y with a caret on top : y hatAnd it’s called “y-hat”

Other Helpful Links

If you haven’t had time to go through Andy’s Visual Vocabulary, take a look at the correlation section.

At the end of the webinar I recommended Bora Beran’s blog for fantastic explanations on Tableau’s modeling features. He has a statistics background and explains the technical in a clear, easy-to-understand format.

Don’t  forget to learn about residual plots if you are using regression to predict.

ice cube quote

 

 

 

 

 

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